{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import codecs\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'batch_size':32,\n",
    "    'lr' : 0.01,\n",
    "    'max_sent_len': 20,\n",
    "    'epochs': 500,\n",
    "    'drops' : [0.1]\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_data(data_path):\n",
    "    \"\"\"\n",
    "    意图识别抽取出label\n",
    "    槽位识别与填充作为命名实体识别问题，对每一个字进行实体标注, ate_time', 'B-target', 'I-date_time', 'I-date_time', 'I-operation', 'I-date_time', 'I-date_time']\n",
    "[ ]:\n",
    "￼\n",
    "​B E I O S#         print(txt_seqs[index])\n",
    "    \"\"\"\n",
    "    with codecs.open(data_path,\"r\",encoding=\"utf-8\") as fp:\n",
    "        data = json.load(fp)\n",
    "    texts = [example['text'].replace(\" \",\"\") for example in data]\n",
    "    intent_labels = [example['intent'] for example in data]\n",
    "    \n",
    "    slots_ners = []\n",
    "    count = 0\n",
    "    for example in data:\n",
    "        if 'entities' in example.keys():\n",
    "            text = example['text']\n",
    "            ner = ['O'] * len(text)\n",
    "            slots = example['entities']\n",
    "            for key,val in slots.items():\n",
    "                start_idx = text.find(val)\n",
    "                end_idx = start_idx + len(val) -1\n",
    "                if len(val) == 1:\n",
    "                    ner[start_idx] = 'S-' + key\n",
    "                else:\n",
    "                    ner[start_idx] = 'B-' + key\n",
    "                    ner[end_idx] = 'E-'+ key\n",
    "                    for idx in range(start_idx+1, end_idx):\n",
    "                        ner[idx] = 'I-' + key\n",
    "        else:\n",
    "            text = example['text']\n",
    "            ner = ['O'] * len(text)\n",
    "        slots_ners.append(ner)\n",
    "    print('texts len: ', len(texts))\n",
    "    print('intent_lables len: ',len(intent_labels))\n",
    "    print('slots_ners len: ', len(slots_ners))\n",
    "    return texts, intent_labels, slots_ners        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "texts len:  2506\n",
      "intent_lables len:  2506\n",
      "slots_ners len:  2506\n"
     ]
    }
   ],
   "source": [
    "data_path =\"../dataset/data_v2.json\"\n",
    "max_sent_len = params[\"max_sent_len\"]\n",
    "texts, intent, slots_ners = extract_data(data_path)\n",
    "l = len(texts) // params['batch_size']\n",
    "texts = texts[:l*params['batch_size']]\n",
    "intent_labels =  intent[:l*params['batch_size']]\n",
    "slots_ners = slots_ners[:l*params['batch_size']]\n",
    "\n",
    "train_text = [d for i , d in enumerate(texts) if i % 10 != 0]\n",
    "train_l = len(train_text) // params['batch_size']\n",
    "train_text = train_text[:train_l*params['batch_size']]\n",
    "\n",
    "valid_text = [d for i , d in enumerate(texts) if i % 10 == 0]\n",
    "valid_l = len(valid_text) // params['batch_size']\n",
    "valid_text = valid_text[:valid_l*params['batch_size']]\n",
    "\n",
    "train_intent = [d for i , d in enumerate(intent_labels) if i % 10 != 0]\n",
    "train_intent = train_intent[:train_l*params['batch_size']]\n",
    "valid_intent = [d for i , d in enumerate(intent_labels) if i % 10 == 0]\n",
    "valid_intent = valid_intent[:valid_l*params['batch_size']]\n",
    "\n",
    "train_ner = [d for i , d in enumerate(slots_ners) if i % 10 != 0]\n",
    "train_ner = train_ner[:train_l*params['batch_size']]\n",
    "valid_ner = [d for i , d in enumerate(slots_ners) if i % 10 == 0]\n",
    "valid_ner =valid_ner[:valid_l*params['batch_size']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('/home/ai/hgm/Smart_Home/ner_model/char_conv.json', mode='r', encoding='utf-8') as f:\n",
    "    dicts = json.load(f)\n",
    "    \n",
    "char2id = dicts['char2id']\n",
    "id2char = dicts['id2char']\n",
    "intent2id = dicts['intent2id']\n",
    "id2intent = dicts['id2intent']\n",
    "slot2id = dicts['slot2id']\n",
    "id2slot = dicts['id2slot']\n",
    "\n",
    "params['intent_num'] = len(intent2id)\n",
    "params['slot_num'] = len(slot2id)\n",
    "params['id2intent'] = id2intent\n",
    "params['id2slot'] = id2slot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans2labelid(vocab, labels, max_sent_len):\n",
    "    labels = [vocab[label] for label in labels]\n",
    "    if len(labels) < max_sent_len:\n",
    "        labels += [0] * (max_sent_len - len(labels))\n",
    "    else:\n",
    "        labels = labels[:max_sent_len]\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(txt_seqs, intent_labels, slot_ners,char2id,intent2id,slot2id,max_sent_len):\n",
    "    dataset_text_labels = []\n",
    "    dataset_intent_labels = []\n",
    "    dataset_ner_labels = []\n",
    "    \n",
    "    for index in range(len(txt_seqs)):\n",
    "        dataset_text_labels.append(trans2labelid(char2id,txt_seqs[index],max_sent_len))\n",
    "        dataset_intent_labels.append([intent2id[intent_labels[index]]])\n",
    "        dataset_ner_labels.append(trans2labelid(slot2id,slot_ners[index],max_sent_len))\n",
    "    dataset_text_labels = np.array(dataset_text_labels)\n",
    "    dataset_intent_labels = np.array(dataset_intent_labels)\n",
    "    dataset_ner_labels = np.array(dataset_ner_labels)\n",
    "    \n",
    "    return dataset_text_labels, dataset_intent_labels, dataset_ner_labels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tarin_seq, train_intent, train_ner =  read_data(texts, intent_labels, slots_ners,char2id,intent2id,slot2id,max_sent_len) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_seq, train_intent, train_ner =  read_data(train_text, train_intent, train_ner,char2id,intent2id,slot2id,max_sent_len) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_seq, valid_intent, valid_ner =  read_data(valid_text, valid_intent, valid_ner,char2id,intent2id,slot2id,max_sent_len) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Dataset(txt_seqs, dataset_intent_labels, dataset_ner_labels):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(({\n",
    "    \"Input\" : txt_seqs\n",
    "    },\n",
    "    {\n",
    "        \"pre_intent\":dataset_intent_labels,\n",
    "        \n",
    "        \"pre_ner\":dataset_ner_labels\n",
    "    }))\n",
    "    l = len(txt_seqs)\n",
    "    dataset = dataset.shuffle(l)\n",
    "    dataset = dataset.batch(params['batch_size'])\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = Dataset(train_seq, train_intent, train_ner)\n",
    "valid_dataset = Dataset(valid_seq, valid_intent, valid_ner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "params['intent_num'] = len(intent2id)\n",
    "params['slot_num'] = len(slot2id)\n",
    "params['id2intent'] = id2intent\n",
    "params['id2slot'] = id2slot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Input (InputLayer)              [(None, 20)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 20, 64)       32000       Input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional (Bidirectional)   (None, 20, 128)      49920       embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization (LayerNorma (None, 20, 128)      256         bidirectional[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d (Globa (None, 128)          0           layer_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_1 (LayerNor (None, 20, 128)      256         bidirectional[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "pre_intent (Dense)              (None, 50)           6450        global_average_pooling1d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "pre_ner (Dense)                 (None, 20, 36)       4644        layer_normalization_1[0][0]      \n",
      "==================================================================================================\n",
      "Total params: 93,526\n",
      "Trainable params: 93,526\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "text_inputs = tf.keras.layers.Input(shape=(20,),name='Input')\n",
    "embed = tf.keras.layers.Embedding(500,64)(text_inputs)\n",
    "bilstm = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64,return_sequences=True))(embed)\n",
    "x_in = tf.keras.layers.LayerNormalization()(bilstm)\n",
    "x_conv = tf.keras.layers.GlobalAveragePooling1D()(x_in)\n",
    "pre_intent = tf.keras.layers.Dense(params['intent_num'],\\\n",
    "            activation='softmax',name = 'pre_intent')(x_conv)\n",
    "x_ner  = tf.keras.layers.LayerNormalization()(bilstm)\n",
    "pre_slot = tf.keras.layers.Dense(params['slot_num'],activation='softmax',name = 'pre_ner')(x_ner)\n",
    "model = tf.keras.Model(text_inputs,[pre_intent,pre_slot])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_loss(y_true,y_pred):\n",
    "    return tf.keras.losses.sparse_categorical_crossentropy(y_true,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = {'pre_intent':'sparse_categorical_crossentropy','pre_ner':'sparse_categorical_crossentropy'}\n",
    "metrics = { 'pre_intent': ['accuracy'],'pre_ner': ['accuracy']}\n",
    "optimizer = tf.keras.optimizers.Adam(params['lr'])\n",
    "model.compile(optimizer, loss=losses, metrics=metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = '../ner_model_weight/model_conv.h5'\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(file_path,\n",
    "                                                        save_weights_only=False, save_best_only=True)\n",
    "learning_rate_reduction = tf.keras.callbacks.ReduceLROnPlateau(patience=50, factor=0.5)\n",
    "callbacks_list = [checkpoint,learning_rate_reduction]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "70/70 [==============================] - 1s 11ms/step - loss: 1.8512 - pre_intent_loss: 1.3472 - pre_ner_loss: 0.5040 - pre_intent_accuracy: 0.6987 - pre_ner_accuracy: 0.8833 - val_loss: 0.7416 - val_pre_intent_loss: 0.4644 - val_pre_ner_loss: 0.2772 - val_pre_intent_accuracy: 0.8795 - val_pre_ner_accuracy: 0.9288\n",
      "Epoch 2/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.3937 - pre_intent_loss: 0.1938 - pre_ner_loss: 0.1999 - pre_intent_accuracy: 0.9438 - pre_ner_accuracy: 0.9492 - val_loss: 0.3995 - val_pre_intent_loss: 0.1985 - val_pre_ner_loss: 0.2010 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9473\n",
      "Epoch 3/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.2187 - pre_intent_loss: 0.0661 - pre_ner_loss: 0.1526 - pre_intent_accuracy: 0.9812 - pre_ner_accuracy: 0.9605 - val_loss: 0.4049 - val_pre_intent_loss: 0.2474 - val_pre_ner_loss: 0.1575 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9616\n",
      "Epoch 4/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.1697 - pre_intent_loss: 0.0481 - pre_ner_loss: 0.1216 - pre_intent_accuracy: 0.9830 - pre_ner_accuracy: 0.9673 - val_loss: 0.3034 - val_pre_intent_loss: 0.1636 - val_pre_ner_loss: 0.1398 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9652\n",
      "Epoch 5/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.1399 - pre_intent_loss: 0.0394 - pre_ner_loss: 0.1005 - pre_intent_accuracy: 0.9879 - pre_ner_accuracy: 0.9713 - val_loss: 0.3334 - val_pre_intent_loss: 0.2046 - val_pre_ner_loss: 0.1288 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9658\n",
      "Epoch 6/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.1254 - pre_intent_loss: 0.0324 - pre_ner_loss: 0.0930 - pre_intent_accuracy: 0.9906 - pre_ner_accuracy: 0.9724 - val_loss: 0.2970 - val_pre_intent_loss: 0.1643 - val_pre_ner_loss: 0.1328 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9634\n",
      "Epoch 7/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.1420 - pre_intent_loss: 0.0480 - pre_ner_loss: 0.0939 - pre_intent_accuracy: 0.9866 - pre_ner_accuracy: 0.9729 - val_loss: 0.3688 - val_pre_intent_loss: 0.2265 - val_pre_ner_loss: 0.1424 - val_pre_intent_accuracy: 0.9241 - val_pre_ner_accuracy: 0.9634\n",
      "Epoch 8/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.1303 - pre_intent_loss: 0.0437 - pre_ner_loss: 0.0866 - pre_intent_accuracy: 0.9866 - pre_ner_accuracy: 0.9744 - val_loss: 0.3519 - val_pre_intent_loss: 0.2141 - val_pre_ner_loss: 0.1378 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9658\n",
      "Epoch 9/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0998 - pre_intent_loss: 0.0246 - pre_ner_loss: 0.0752 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9774 - val_loss: 0.3293 - val_pre_intent_loss: 0.2168 - val_pre_ner_loss: 0.1125 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 10/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0892 - pre_intent_loss: 0.0266 - pre_ner_loss: 0.0626 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9807 - val_loss: 0.3461 - val_pre_intent_loss: 0.2141 - val_pre_ner_loss: 0.1320 - val_pre_intent_accuracy: 0.9598 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 11/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0850 - pre_intent_loss: 0.0263 - pre_ner_loss: 0.0587 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9812 - val_loss: 0.3482 - val_pre_intent_loss: 0.2225 - val_pre_ner_loss: 0.1257 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 12/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0778 - pre_intent_loss: 0.0226 - pre_ner_loss: 0.0551 - pre_intent_accuracy: 0.9911 - pre_ner_accuracy: 0.9817 - val_loss: 0.3816 - val_pre_intent_loss: 0.2495 - val_pre_ner_loss: 0.1321 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 13/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0670 - pre_intent_loss: 0.0159 - pre_ner_loss: 0.0511 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9830 - val_loss: 0.3565 - val_pre_intent_loss: 0.2282 - val_pre_ner_loss: 0.1283 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 14/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0671 - pre_intent_loss: 0.0194 - pre_ner_loss: 0.0477 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9840 - val_loss: 0.3914 - val_pre_intent_loss: 0.2641 - val_pre_ner_loss: 0.1272 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 15/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0623 - pre_intent_loss: 0.0162 - pre_ner_loss: 0.0461 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9839 - val_loss: 0.3568 - val_pre_intent_loss: 0.2131 - val_pre_ner_loss: 0.1436 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 16/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0688 - pre_intent_loss: 0.0211 - pre_ner_loss: 0.0476 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9846 - val_loss: 0.3272 - val_pre_intent_loss: 0.2075 - val_pre_ner_loss: 0.1197 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 17/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0608 - pre_intent_loss: 0.0162 - pre_ner_loss: 0.0446 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9844 - val_loss: 0.3624 - val_pre_intent_loss: 0.2383 - val_pre_ner_loss: 0.1241 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 18/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0540 - pre_intent_loss: 0.0148 - pre_ner_loss: 0.0392 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9866 - val_loss: 0.4226 - val_pre_intent_loss: 0.2800 - val_pre_ner_loss: 0.1426 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 19/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0511 - pre_intent_loss: 0.0143 - pre_ner_loss: 0.0368 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9868 - val_loss: 0.3557 - val_pre_intent_loss: 0.2373 - val_pre_ner_loss: 0.1184 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9750\n",
      "Epoch 20/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0465 - pre_intent_loss: 0.0113 - pre_ner_loss: 0.0352 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9876 - val_loss: 0.3966 - val_pre_intent_loss: 0.2589 - val_pre_ner_loss: 0.1378 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 21/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0496 - pre_intent_loss: 0.0162 - pre_ner_loss: 0.0334 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9879 - val_loss: 0.3668 - val_pre_intent_loss: 0.2346 - val_pre_ner_loss: 0.1322 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 22/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0422 - pre_intent_loss: 0.0127 - pre_ner_loss: 0.0295 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9894 - val_loss: 0.4043 - val_pre_intent_loss: 0.2758 - val_pre_ner_loss: 0.1285 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 23/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0364 - pre_intent_loss: 0.0112 - pre_ner_loss: 0.0251 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9911 - val_loss: 0.3760 - val_pre_intent_loss: 0.2433 - val_pre_ner_loss: 0.1328 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9748\n",
      "Epoch 24/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0343 - pre_intent_loss: 0.0101 - pre_ner_loss: 0.0242 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9905 - val_loss: 0.4017 - val_pre_intent_loss: 0.2649 - val_pre_ner_loss: 0.1368 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 25/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0352 - pre_intent_loss: 0.0089 - pre_ner_loss: 0.0263 - pre_intent_accuracy: 0.9960 - pre_ner_accuracy: 0.9901 - val_loss: 0.3445 - val_pre_intent_loss: 0.2162 - val_pre_ner_loss: 0.1283 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9766\n",
      "Epoch 26/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0370 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0252 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9904 - val_loss: 0.3930 - val_pre_intent_loss: 0.2595 - val_pre_ner_loss: 0.1335 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9746\n",
      "Epoch 27/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0374 - pre_intent_loss: 0.0134 - pre_ner_loss: 0.0239 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9910 - val_loss: 0.4121 - val_pre_intent_loss: 0.2724 - val_pre_ner_loss: 0.1397 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 28/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0421 - pre_intent_loss: 0.0130 - pre_ner_loss: 0.0291 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9903 - val_loss: 0.4050 - val_pre_intent_loss: 0.2643 - val_pre_ner_loss: 0.1408 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 29/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0351 - pre_intent_loss: 0.0099 - pre_ner_loss: 0.0251 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9912 - val_loss: 0.4094 - val_pre_intent_loss: 0.2660 - val_pre_ner_loss: 0.1434 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 30/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0339 - pre_intent_loss: 0.0090 - pre_ner_loss: 0.0249 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9911 - val_loss: 0.4684 - val_pre_intent_loss: 0.3147 - val_pre_ner_loss: 0.1537 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9743\n",
      "Epoch 31/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0366 - pre_intent_loss: 0.0126 - pre_ner_loss: 0.0240 - pre_intent_accuracy: 0.9955 - pre_ner_accuracy: 0.9921 - val_loss: 0.3665 - val_pre_intent_loss: 0.2126 - val_pre_ner_loss: 0.1539 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 32/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0431 - pre_intent_loss: 0.0171 - pre_ner_loss: 0.0260 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9909 - val_loss: 0.4103 - val_pre_intent_loss: 0.2619 - val_pre_ner_loss: 0.1483 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 33/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0377 - pre_intent_loss: 0.0134 - pre_ner_loss: 0.0243 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9916 - val_loss: 0.4514 - val_pre_intent_loss: 0.2946 - val_pre_ner_loss: 0.1569 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 34/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0797 - pre_intent_loss: 0.0386 - pre_ner_loss: 0.0412 - pre_intent_accuracy: 0.9879 - pre_ner_accuracy: 0.9874 - val_loss: 0.6366 - val_pre_intent_loss: 0.4379 - val_pre_ner_loss: 0.1987 - val_pre_intent_accuracy: 0.9107 - val_pre_ner_accuracy: 0.9614\n",
      "Epoch 35/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.2882 - pre_intent_loss: 0.1450 - pre_ner_loss: 0.1432 - pre_intent_accuracy: 0.9612 - pre_ner_accuracy: 0.9611 - val_loss: 0.5305 - val_pre_intent_loss: 0.3229 - val_pre_ner_loss: 0.2076 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9509\n",
      "Epoch 36/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.2325 - pre_intent_loss: 0.1080 - pre_ner_loss: 0.1245 - pre_intent_accuracy: 0.9683 - pre_ner_accuracy: 0.9649 - val_loss: 0.6496 - val_pre_intent_loss: 0.4845 - val_pre_ner_loss: 0.1652 - val_pre_intent_accuracy: 0.9196 - val_pre_ner_accuracy: 0.9614\n",
      "Epoch 37/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.1586 - pre_intent_loss: 0.0622 - pre_ner_loss: 0.0964 - pre_intent_accuracy: 0.9812 - pre_ner_accuracy: 0.9722 - val_loss: 0.5347 - val_pre_intent_loss: 0.3786 - val_pre_ner_loss: 0.1561 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9612\n",
      "Epoch 38/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.1170 - pre_intent_loss: 0.0441 - pre_ner_loss: 0.0729 - pre_intent_accuracy: 0.9871 - pre_ner_accuracy: 0.9778 - val_loss: 0.4470 - val_pre_intent_loss: 0.3091 - val_pre_ner_loss: 0.1379 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9679\n",
      "Epoch 39/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0775 - pre_intent_loss: 0.0199 - pre_ner_loss: 0.0576 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9821 - val_loss: 0.4041 - val_pre_intent_loss: 0.2693 - val_pre_ner_loss: 0.1348 - val_pre_intent_accuracy: 0.9554 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 40/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0639 - pre_intent_loss: 0.0191 - pre_ner_loss: 0.0448 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9855 - val_loss: 0.4691 - val_pre_intent_loss: 0.3283 - val_pre_ner_loss: 0.1408 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9681\n",
      "Epoch 41/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0563 - pre_intent_loss: 0.0192 - pre_ner_loss: 0.0371 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9868 - val_loss: 0.4671 - val_pre_intent_loss: 0.3251 - val_pre_ner_loss: 0.1420 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9681\n",
      "Epoch 42/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0501 - pre_intent_loss: 0.0171 - pre_ner_loss: 0.0331 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9882 - val_loss: 0.4769 - val_pre_intent_loss: 0.3368 - val_pre_ner_loss: 0.1401 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9696\n",
      "Epoch 43/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0476 - pre_intent_loss: 0.0202 - pre_ner_loss: 0.0274 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9901 - val_loss: 0.4303 - val_pre_intent_loss: 0.2863 - val_pre_ner_loss: 0.1440 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9692\n",
      "Epoch 44/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0400 - pre_intent_loss: 0.0136 - pre_ner_loss: 0.0264 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9906 - val_loss: 0.3941 - val_pre_intent_loss: 0.2518 - val_pre_ner_loss: 0.1423 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 45/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0400 - pre_intent_loss: 0.0167 - pre_ner_loss: 0.0233 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9911 - val_loss: 0.4401 - val_pre_intent_loss: 0.2944 - val_pre_ner_loss: 0.1457 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 46/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0360 - pre_intent_loss: 0.0138 - pre_ner_loss: 0.0222 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9914 - val_loss: 0.4059 - val_pre_intent_loss: 0.2565 - val_pre_ner_loss: 0.1494 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9692\n",
      "Epoch 47/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0353 - pre_intent_loss: 0.0132 - pre_ner_loss: 0.0221 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9918 - val_loss: 0.4296 - val_pre_intent_loss: 0.2740 - val_pre_ner_loss: 0.1556 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9681\n",
      "Epoch 48/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0350 - pre_intent_loss: 0.0135 - pre_ner_loss: 0.0215 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9920 - val_loss: 0.4258 - val_pre_intent_loss: 0.2707 - val_pre_ner_loss: 0.1551 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 49/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0355 - pre_intent_loss: 0.0121 - pre_ner_loss: 0.0234 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9922 - val_loss: 0.4620 - val_pre_intent_loss: 0.3038 - val_pre_ner_loss: 0.1582 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9647\n",
      "Epoch 50/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0386 - pre_intent_loss: 0.0126 - pre_ner_loss: 0.0260 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9910 - val_loss: 0.4328 - val_pre_intent_loss: 0.2734 - val_pre_ner_loss: 0.1594 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9690\n",
      "Epoch 51/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0381 - pre_intent_loss: 0.0137 - pre_ner_loss: 0.0244 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9910 - val_loss: 0.4127 - val_pre_intent_loss: 0.2504 - val_pre_ner_loss: 0.1623 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9676\n",
      "Epoch 52/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0334 - pre_intent_loss: 0.0125 - pre_ner_loss: 0.0210 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9925 - val_loss: 0.4512 - val_pre_intent_loss: 0.2830 - val_pre_ner_loss: 0.1681 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 53/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0310 - pre_intent_loss: 0.0111 - pre_ner_loss: 0.0200 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9930 - val_loss: 0.4132 - val_pre_intent_loss: 0.2553 - val_pre_ner_loss: 0.1579 - val_pre_intent_accuracy: 0.9509 - val_pre_ner_accuracy: 0.9696\n",
      "Epoch 54/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0313 - pre_intent_loss: 0.0106 - pre_ner_loss: 0.0207 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9925 - val_loss: 0.4335 - val_pre_intent_loss: 0.2556 - val_pre_ner_loss: 0.1779 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 55/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0311 - pre_intent_loss: 0.0117 - pre_ner_loss: 0.0194 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9933 - val_loss: 0.4653 - val_pre_intent_loss: 0.2867 - val_pre_ner_loss: 0.1786 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9696\n",
      "Epoch 56/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0290 - pre_intent_loss: 0.0101 - pre_ner_loss: 0.0189 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9935 - val_loss: 0.4651 - val_pre_intent_loss: 0.2873 - val_pre_ner_loss: 0.1778 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9688\n",
      "Epoch 57/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0214 - pre_intent_loss: 0.0090 - pre_ner_loss: 0.0123 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9951 - val_loss: 0.4653 - val_pre_intent_loss: 0.2924 - val_pre_ner_loss: 0.1729 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9701\n",
      "Epoch 58/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0193 - pre_intent_loss: 0.0099 - pre_ner_loss: 0.0094 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9963 - val_loss: 0.4619 - val_pre_intent_loss: 0.2912 - val_pre_ner_loss: 0.1707 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 59/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0169 - pre_intent_loss: 0.0086 - pre_ner_loss: 0.0083 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9964 - val_loss: 0.4658 - val_pre_intent_loss: 0.2923 - val_pre_ner_loss: 0.1735 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 60/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0163 - pre_intent_loss: 0.0085 - pre_ner_loss: 0.0078 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9964 - val_loss: 0.4691 - val_pre_intent_loss: 0.2935 - val_pre_ner_loss: 0.1755 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 61/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0181 - pre_intent_loss: 0.0100 - pre_ner_loss: 0.0082 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9963 - val_loss: 0.4690 - val_pre_intent_loss: 0.2955 - val_pre_ner_loss: 0.1735 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 62/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0173 - pre_intent_loss: 0.0094 - pre_ner_loss: 0.0080 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.4645 - val_pre_intent_loss: 0.2902 - val_pre_ner_loss: 0.1743 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 63/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0160 - pre_intent_loss: 0.0086 - pre_ner_loss: 0.0074 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.4624 - val_pre_intent_loss: 0.2828 - val_pre_ner_loss: 0.1796 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 64/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0154 - pre_intent_loss: 0.0082 - pre_ner_loss: 0.0072 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.4685 - val_pre_intent_loss: 0.2901 - val_pre_ner_loss: 0.1784 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 65/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0170 - pre_intent_loss: 0.0093 - pre_ner_loss: 0.0077 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.4627 - val_pre_intent_loss: 0.2809 - val_pre_ner_loss: 0.1818 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 66/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0170 - pre_intent_loss: 0.0094 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9968 - val_loss: 0.4747 - val_pre_intent_loss: 0.2892 - val_pre_ner_loss: 0.1855 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 67/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0159 - pre_intent_loss: 0.0085 - pre_ner_loss: 0.0073 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.4536 - val_pre_intent_loss: 0.2775 - val_pre_ner_loss: 0.1762 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 68/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0162 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0078 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9963 - val_loss: 0.4640 - val_pre_intent_loss: 0.2862 - val_pre_ner_loss: 0.1779 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 69/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0174 - pre_intent_loss: 0.0091 - pre_ner_loss: 0.0083 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.4748 - val_pre_intent_loss: 0.2872 - val_pre_ner_loss: 0.1877 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 70/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0171 - pre_intent_loss: 0.0089 - pre_ner_loss: 0.0083 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9964 - val_loss: 0.4725 - val_pre_intent_loss: 0.2914 - val_pre_ner_loss: 0.1811 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 71/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0169 - pre_intent_loss: 0.0093 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.4632 - val_pre_intent_loss: 0.2852 - val_pre_ner_loss: 0.1780 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 72/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0167 - pre_intent_loss: 0.0090 - pre_ner_loss: 0.0078 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9963 - val_loss: 0.4655 - val_pre_intent_loss: 0.2812 - val_pre_ner_loss: 0.1843 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 73/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0173 - pre_intent_loss: 0.0098 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9964 - val_loss: 0.4691 - val_pre_intent_loss: 0.2817 - val_pre_ner_loss: 0.1874 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 74/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0162 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0079 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9966 - val_loss: 0.4674 - val_pre_intent_loss: 0.2854 - val_pre_ner_loss: 0.1820 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 75/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0169 - pre_intent_loss: 0.0090 - pre_ner_loss: 0.0079 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9965 - val_loss: 0.4737 - val_pre_intent_loss: 0.2865 - val_pre_ner_loss: 0.1872 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 76/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0165 - pre_intent_loss: 0.0091 - pre_ner_loss: 0.0074 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.4731 - val_pre_intent_loss: 0.2871 - val_pre_ner_loss: 0.1860 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 77/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0161 - pre_intent_loss: 0.0083 - pre_ner_loss: 0.0078 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9962 - val_loss: 0.4774 - val_pre_intent_loss: 0.2918 - val_pre_ner_loss: 0.1856 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 78/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0161 - pre_intent_loss: 0.0087 - pre_ner_loss: 0.0074 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.4719 - val_pre_intent_loss: 0.2868 - val_pre_ner_loss: 0.1851 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 79/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0154 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0069 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.4704 - val_pre_intent_loss: 0.2868 - val_pre_ner_loss: 0.1837 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 80/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0162 - pre_intent_loss: 0.0088 - pre_ner_loss: 0.0074 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.4682 - val_pre_intent_loss: 0.2829 - val_pre_ner_loss: 0.1853 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 81/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0172 - pre_intent_loss: 0.0090 - pre_ner_loss: 0.0082 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.4845 - val_pre_intent_loss: 0.2900 - val_pre_ner_loss: 0.1945 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 82/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0172 - pre_intent_loss: 0.0087 - pre_ner_loss: 0.0085 - pre_intent_accuracy: 0.9960 - pre_ner_accuracy: 0.9964 - val_loss: 0.4690 - val_pre_intent_loss: 0.2758 - val_pre_ner_loss: 0.1932 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 83/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0196 - pre_intent_loss: 0.0100 - pre_ner_loss: 0.0097 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9958 - val_loss: 0.4791 - val_pre_intent_loss: 0.2852 - val_pre_ner_loss: 0.1939 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9703\n",
      "Epoch 84/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0266 - pre_intent_loss: 0.0097 - pre_ner_loss: 0.0170 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9940 - val_loss: 0.4753 - val_pre_intent_loss: 0.2880 - val_pre_ner_loss: 0.1873 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9672\n",
      "Epoch 85/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0449 - pre_intent_loss: 0.0096 - pre_ner_loss: 0.0353 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9883 - val_loss: 0.5068 - val_pre_intent_loss: 0.2990 - val_pre_ner_loss: 0.2078 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9650\n",
      "Epoch 86/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0542 - pre_intent_loss: 0.0116 - pre_ner_loss: 0.0425 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9870 - val_loss: 0.4955 - val_pre_intent_loss: 0.3169 - val_pre_ner_loss: 0.1786 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9672\n",
      "Epoch 87/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0435 - pre_intent_loss: 0.0102 - pre_ner_loss: 0.0333 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9886 - val_loss: 0.4617 - val_pre_intent_loss: 0.2885 - val_pre_ner_loss: 0.1732 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9696\n",
      "Epoch 88/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0331 - pre_intent_loss: 0.0092 - pre_ner_loss: 0.0239 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9913 - val_loss: 0.4927 - val_pre_intent_loss: 0.3269 - val_pre_ner_loss: 0.1658 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 89/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0295 - pre_intent_loss: 0.0105 - pre_ner_loss: 0.0189 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9927 - val_loss: 0.4620 - val_pre_intent_loss: 0.2765 - val_pre_ner_loss: 0.1855 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9652\n",
      "Epoch 90/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0273 - pre_intent_loss: 0.0095 - pre_ner_loss: 0.0178 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9936 - val_loss: 0.4714 - val_pre_intent_loss: 0.3062 - val_pre_ner_loss: 0.1652 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 91/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0241 - pre_intent_loss: 0.0093 - pre_ner_loss: 0.0147 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9948 - val_loss: 0.4766 - val_pre_intent_loss: 0.3003 - val_pre_ner_loss: 0.1763 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9705\n",
      "Epoch 92/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0204 - pre_intent_loss: 0.0092 - pre_ner_loss: 0.0112 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9954 - val_loss: 0.4709 - val_pre_intent_loss: 0.2981 - val_pre_ner_loss: 0.1728 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 93/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0211 - pre_intent_loss: 0.0094 - pre_ner_loss: 0.0117 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9953 - val_loss: 0.4810 - val_pre_intent_loss: 0.3087 - val_pre_ner_loss: 0.1723 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 94/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0196 - pre_intent_loss: 0.0093 - pre_ner_loss: 0.0103 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9960 - val_loss: 0.4629 - val_pre_intent_loss: 0.2945 - val_pre_ner_loss: 0.1685 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 95/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0185 - pre_intent_loss: 0.0092 - pre_ner_loss: 0.0093 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9963 - val_loss: 0.4689 - val_pre_intent_loss: 0.3000 - val_pre_ner_loss: 0.1688 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 96/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0163 - pre_intent_loss: 0.0086 - pre_ner_loss: 0.0077 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9967 - val_loss: 0.4644 - val_pre_intent_loss: 0.2933 - val_pre_ner_loss: 0.1711 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 97/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0163 - pre_intent_loss: 0.0089 - pre_ner_loss: 0.0074 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.4874 - val_pre_intent_loss: 0.3108 - val_pre_ner_loss: 0.1766 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 98/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0153 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0069 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.4828 - val_pre_intent_loss: 0.3061 - val_pre_ner_loss: 0.1767 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 99/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0160 - pre_intent_loss: 0.0086 - pre_ner_loss: 0.0074 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.4807 - val_pre_intent_loss: 0.3057 - val_pre_ner_loss: 0.1750 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 100/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0159 - pre_intent_loss: 0.0083 - pre_ner_loss: 0.0076 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9964 - val_loss: 0.4782 - val_pre_intent_loss: 0.3014 - val_pre_ner_loss: 0.1768 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 101/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0152 - pre_intent_loss: 0.0083 - pre_ner_loss: 0.0069 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.4736 - val_pre_intent_loss: 0.3000 - val_pre_ner_loss: 0.1736 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 102/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0169 - pre_intent_loss: 0.0085 - pre_ner_loss: 0.0085 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.4722 - val_pre_intent_loss: 0.2978 - val_pre_ner_loss: 0.1743 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 103/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0161 - pre_intent_loss: 0.0084 - pre_ner_loss: 0.0077 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.4791 - val_pre_intent_loss: 0.3018 - val_pre_ner_loss: 0.1773 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 104/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0171 - pre_intent_loss: 0.0093 - pre_ner_loss: 0.0078 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9964 - val_loss: 0.4794 - val_pre_intent_loss: 0.2973 - val_pre_ner_loss: 0.1821 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 105/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0162 - pre_intent_loss: 0.0089 - pre_ner_loss: 0.0073 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.4804 - val_pre_intent_loss: 0.2971 - val_pre_ner_loss: 0.1832 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 106/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0165 - pre_intent_loss: 0.0088 - pre_ner_loss: 0.0077 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.4839 - val_pre_intent_loss: 0.2949 - val_pre_ner_loss: 0.1889 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9710\n",
      "Epoch 107/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0140 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0061 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9968 - val_loss: 0.4824 - val_pre_intent_loss: 0.2971 - val_pre_ner_loss: 0.1854 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 108/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0129 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.4818 - val_pre_intent_loss: 0.2990 - val_pre_ner_loss: 0.1828 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 109/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0130 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.4831 - val_pre_intent_loss: 0.2991 - val_pre_ner_loss: 0.1840 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 110/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0052 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9969 - val_loss: 0.4820 - val_pre_intent_loss: 0.2991 - val_pre_ner_loss: 0.1829 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 111/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0126 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0049 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9971 - val_loss: 0.4866 - val_pre_intent_loss: 0.2996 - val_pre_ner_loss: 0.1870 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 112/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0132 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9965 - val_loss: 0.4863 - val_pre_intent_loss: 0.3021 - val_pre_ner_loss: 0.1842 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 113/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9963 - val_loss: 0.4892 - val_pre_intent_loss: 0.3028 - val_pre_ner_loss: 0.1864 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 114/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0128 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0051 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9968 - val_loss: 0.4904 - val_pre_intent_loss: 0.3044 - val_pre_ner_loss: 0.1860 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 115/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0131 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.4896 - val_pre_intent_loss: 0.3022 - val_pre_ner_loss: 0.1874 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 116/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0131 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.4872 - val_pre_intent_loss: 0.3004 - val_pre_ner_loss: 0.1868 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 117/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.4887 - val_pre_intent_loss: 0.3000 - val_pre_ner_loss: 0.1887 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 118/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0125 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0051 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.4925 - val_pre_intent_loss: 0.3045 - val_pre_ner_loss: 0.1880 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 119/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0130 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.4909 - val_pre_intent_loss: 0.3042 - val_pre_ner_loss: 0.1867 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 120/500\n",
      "70/70 [==============================] - 1s 9ms/step - loss: 0.0131 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.4928 - val_pre_intent_loss: 0.3041 - val_pre_ner_loss: 0.1888 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 121/500\n",
      "70/70 [==============================] - 0s 6ms/step - loss: 0.0136 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.4907 - val_pre_intent_loss: 0.3027 - val_pre_ner_loss: 0.1880 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 122/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0128 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0052 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9971 - val_loss: 0.4922 - val_pre_intent_loss: 0.3024 - val_pre_ner_loss: 0.1898 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 123/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0131 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.4941 - val_pre_intent_loss: 0.3038 - val_pre_ner_loss: 0.1903 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 124/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0126 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0051 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9971 - val_loss: 0.4935 - val_pre_intent_loss: 0.3020 - val_pre_ner_loss: 0.1915 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 125/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0133 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.4943 - val_pre_intent_loss: 0.3050 - val_pre_ner_loss: 0.1893 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 126/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0136 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0059 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9965 - val_loss: 0.4938 - val_pre_intent_loss: 0.3042 - val_pre_ner_loss: 0.1897 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 127/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0133 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.4942 - val_pre_intent_loss: 0.3035 - val_pre_ner_loss: 0.1907 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 128/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0136 - pre_intent_loss: 0.0081 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9965 - val_loss: 0.4907 - val_pre_intent_loss: 0.3030 - val_pre_ner_loss: 0.1877 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 129/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0131 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.4892 - val_pre_intent_loss: 0.3013 - val_pre_ner_loss: 0.1878 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 130/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0134 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.4932 - val_pre_intent_loss: 0.3023 - val_pre_ner_loss: 0.1909 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 131/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0136 - pre_intent_loss: 0.0081 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.4888 - val_pre_intent_loss: 0.3020 - val_pre_ner_loss: 0.1869 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 132/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0131 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.4958 - val_pre_intent_loss: 0.3015 - val_pre_ner_loss: 0.1943 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 133/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9970 - val_loss: 0.4902 - val_pre_intent_loss: 0.2980 - val_pre_ner_loss: 0.1922 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 134/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0133 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.4915 - val_pre_intent_loss: 0.2989 - val_pre_ner_loss: 0.1925 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 135/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0131 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.4955 - val_pre_intent_loss: 0.2999 - val_pre_ner_loss: 0.1957 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 136/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0057 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9963 - val_loss: 0.4999 - val_pre_intent_loss: 0.3020 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 137/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0134 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0057 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.4922 - val_pre_intent_loss: 0.2969 - val_pre_ner_loss: 0.1953 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 138/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0078 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.4911 - val_pre_intent_loss: 0.2986 - val_pre_ner_loss: 0.1925 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 139/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9964 - val_loss: 0.4991 - val_pre_intent_loss: 0.3031 - val_pre_ner_loss: 0.1961 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9732\n",
      "Epoch 140/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0139 - pre_intent_loss: 0.0081 - pre_ner_loss: 0.0058 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.4974 - val_pre_intent_loss: 0.3026 - val_pre_ner_loss: 0.1948 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 141/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9970 - val_loss: 0.4941 - val_pre_intent_loss: 0.2998 - val_pre_ner_loss: 0.1943 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 142/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0137 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0058 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.4953 - val_pre_intent_loss: 0.2985 - val_pre_ner_loss: 0.1968 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 143/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0131 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0052 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.4994 - val_pre_intent_loss: 0.3039 - val_pre_ner_loss: 0.1955 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 144/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0132 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5036 - val_pre_intent_loss: 0.3058 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 145/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0139 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0060 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9965 - val_loss: 0.5014 - val_pre_intent_loss: 0.3030 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9739\n",
      "Epoch 146/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0056 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9963 - val_loss: 0.4952 - val_pre_intent_loss: 0.3006 - val_pre_ner_loss: 0.1945 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 147/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0132 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5020 - val_pre_intent_loss: 0.3045 - val_pre_ner_loss: 0.1975 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 148/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0129 - pre_intent_loss: 0.0077 - pre_ner_loss: 0.0052 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.5020 - val_pre_intent_loss: 0.3072 - val_pre_ner_loss: 0.1948 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 149/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0124 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0051 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9967 - val_loss: 0.5018 - val_pre_intent_loss: 0.3088 - val_pre_ner_loss: 0.1930 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 150/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0135 - pre_intent_loss: 0.0080 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5050 - val_pre_intent_loss: 0.3083 - val_pre_ner_loss: 0.1966 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9737\n",
      "Epoch 151/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0136 - pre_intent_loss: 0.0081 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5019 - val_pre_intent_loss: 0.3095 - val_pre_ner_loss: 0.1924 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 152/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0133 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5051 - val_pre_intent_loss: 0.3085 - val_pre_ner_loss: 0.1965 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 153/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0130 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5041 - val_pre_intent_loss: 0.3053 - val_pre_ner_loss: 0.1989 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 154/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0130 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0055 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.5093 - val_pre_intent_loss: 0.3101 - val_pre_ner_loss: 0.1992 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 155/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0132 - pre_intent_loss: 0.0079 - pre_ner_loss: 0.0053 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.5060 - val_pre_intent_loss: 0.3076 - val_pre_ner_loss: 0.1983 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 156/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0129 - pre_intent_loss: 0.0076 - pre_ner_loss: 0.0054 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5067 - val_pre_intent_loss: 0.3073 - val_pre_ner_loss: 0.1994 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 157/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0120 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5044 - val_pre_intent_loss: 0.3065 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9464 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 158/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5072 - val_pre_intent_loss: 0.3095 - val_pre_ner_loss: 0.1978 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9734\n",
      "Epoch 159/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5071 - val_pre_intent_loss: 0.3086 - val_pre_ner_loss: 0.1985 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 160/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0115 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5098 - val_pre_intent_loss: 0.3103 - val_pre_ner_loss: 0.1994 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 161/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.5101 - val_pre_intent_loss: 0.3122 - val_pre_ner_loss: 0.1979 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 162/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.5079 - val_pre_intent_loss: 0.3095 - val_pre_ner_loss: 0.1984 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 163/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5121 - val_pre_intent_loss: 0.3106 - val_pre_ner_loss: 0.2015 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 164/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9973 - val_loss: 0.5103 - val_pre_intent_loss: 0.3114 - val_pre_ner_loss: 0.1989 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 165/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.5114 - val_pre_intent_loss: 0.3108 - val_pre_ner_loss: 0.2006 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 166/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5118 - val_pre_intent_loss: 0.3124 - val_pre_ner_loss: 0.1994 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 167/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.5131 - val_pre_intent_loss: 0.3131 - val_pre_ner_loss: 0.2000 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 168/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5153 - val_pre_intent_loss: 0.3146 - val_pre_ner_loss: 0.2007 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 169/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0120 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5134 - val_pre_intent_loss: 0.3131 - val_pre_ner_loss: 0.2004 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 170/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9970 - val_loss: 0.5159 - val_pre_intent_loss: 0.3143 - val_pre_ner_loss: 0.2016 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 171/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.5167 - val_pre_intent_loss: 0.3158 - val_pre_ner_loss: 0.2010 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 172/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9970 - val_loss: 0.5160 - val_pre_intent_loss: 0.3155 - val_pre_ner_loss: 0.2005 - val_pre_intent_accuracy: 0.9420 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 173/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0120 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9964 - val_loss: 0.5162 - val_pre_intent_loss: 0.3143 - val_pre_ner_loss: 0.2018 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 174/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5171 - val_pre_intent_loss: 0.3161 - val_pre_ner_loss: 0.2009 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 175/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0116 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5173 - val_pre_intent_loss: 0.3152 - val_pre_ner_loss: 0.2021 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 176/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5170 - val_pre_intent_loss: 0.3156 - val_pre_ner_loss: 0.2015 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 177/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5204 - val_pre_intent_loss: 0.3175 - val_pre_ner_loss: 0.2029 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9708\n",
      "Epoch 178/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0121 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0048 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9965 - val_loss: 0.5229 - val_pre_intent_loss: 0.3186 - val_pre_ner_loss: 0.2043 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 179/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5200 - val_pre_intent_loss: 0.3174 - val_pre_ner_loss: 0.2025 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 180/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5195 - val_pre_intent_loss: 0.3164 - val_pre_ner_loss: 0.2031 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 181/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5216 - val_pre_intent_loss: 0.3192 - val_pre_ner_loss: 0.2025 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 182/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0120 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5218 - val_pre_intent_loss: 0.3158 - val_pre_ner_loss: 0.2060 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 183/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5254 - val_pre_intent_loss: 0.3222 - val_pre_ner_loss: 0.2032 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 184/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9964 - val_loss: 0.5245 - val_pre_intent_loss: 0.3207 - val_pre_ner_loss: 0.2038 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 185/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5268 - val_pre_intent_loss: 0.3228 - val_pre_ner_loss: 0.2040 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 186/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.5271 - val_pre_intent_loss: 0.3242 - val_pre_ner_loss: 0.2030 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 187/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5253 - val_pre_intent_loss: 0.3213 - val_pre_ner_loss: 0.2040 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 188/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0121 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5281 - val_pre_intent_loss: 0.3232 - val_pre_ner_loss: 0.2049 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 189/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5282 - val_pre_intent_loss: 0.3243 - val_pre_ner_loss: 0.2038 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 190/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5265 - val_pre_intent_loss: 0.3232 - val_pre_ner_loss: 0.2033 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 191/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0121 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.5265 - val_pre_intent_loss: 0.3239 - val_pre_ner_loss: 0.2026 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 192/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0120 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.5261 - val_pre_intent_loss: 0.3241 - val_pre_ner_loss: 0.2020 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 193/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0116 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0043 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5251 - val_pre_intent_loss: 0.3218 - val_pre_ner_loss: 0.2033 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 194/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0122 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0047 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5264 - val_pre_intent_loss: 0.3221 - val_pre_ner_loss: 0.2043 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 195/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0118 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5231 - val_pre_intent_loss: 0.3192 - val_pre_ner_loss: 0.2039 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 196/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5284 - val_pre_intent_loss: 0.3240 - val_pre_ner_loss: 0.2044 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 197/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9967 - val_loss: 0.5278 - val_pre_intent_loss: 0.3240 - val_pre_ner_loss: 0.2038 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 198/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0116 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5255 - val_pre_intent_loss: 0.3215 - val_pre_ner_loss: 0.2040 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 199/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0118 - pre_intent_loss: 0.0074 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.5276 - val_pre_intent_loss: 0.3248 - val_pre_ner_loss: 0.2028 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 200/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0118 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5273 - val_pre_intent_loss: 0.3216 - val_pre_ner_loss: 0.2057 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 201/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5296 - val_pre_intent_loss: 0.3246 - val_pre_ner_loss: 0.2050 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 202/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9915 - pre_ner_accuracy: 0.9968 - val_loss: 0.5297 - val_pre_intent_loss: 0.3247 - val_pre_ner_loss: 0.2050 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 203/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0117 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0044 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5319 - val_pre_intent_loss: 0.3275 - val_pre_ner_loss: 0.2044 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 204/500\n",
      "70/70 [==============================] - 0s 6ms/step - loss: 0.0119 - pre_intent_loss: 0.0073 - pre_ner_loss: 0.0046 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5320 - val_pre_intent_loss: 0.3265 - val_pre_ner_loss: 0.2055 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 205/500\n",
      "70/70 [==============================] - 0s 6ms/step - loss: 0.0117 - pre_intent_loss: 0.0072 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9964 - val_loss: 0.5333 - val_pre_intent_loss: 0.3281 - val_pre_ner_loss: 0.2052 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 206/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0120 - pre_intent_loss: 0.0075 - pre_ner_loss: 0.0045 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5338 - val_pre_intent_loss: 0.3273 - val_pre_ner_loss: 0.2065 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 207/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5340 - val_pre_intent_loss: 0.3276 - val_pre_ner_loss: 0.2064 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 208/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5342 - val_pre_intent_loss: 0.3277 - val_pre_ner_loss: 0.2065 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 209/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5363 - val_pre_intent_loss: 0.3292 - val_pre_ner_loss: 0.2071 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 210/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9966 - val_loss: 0.5350 - val_pre_intent_loss: 0.3286 - val_pre_ner_loss: 0.2063 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 211/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9964 - val_loss: 0.5372 - val_pre_intent_loss: 0.3304 - val_pre_ner_loss: 0.2067 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 212/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.5389 - val_pre_intent_loss: 0.3315 - val_pre_ner_loss: 0.2074 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 213/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5394 - val_pre_intent_loss: 0.3321 - val_pre_ner_loss: 0.2073 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 214/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5403 - val_pre_intent_loss: 0.3327 - val_pre_ner_loss: 0.2076 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 215/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5393 - val_pre_intent_loss: 0.3317 - val_pre_ner_loss: 0.2076 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 216/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5397 - val_pre_intent_loss: 0.3323 - val_pre_ner_loss: 0.2074 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 217/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5404 - val_pre_intent_loss: 0.3328 - val_pre_ner_loss: 0.2075 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 218/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5418 - val_pre_intent_loss: 0.3338 - val_pre_ner_loss: 0.2080 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 219/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9965 - val_loss: 0.5423 - val_pre_intent_loss: 0.3350 - val_pre_ner_loss: 0.2073 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 220/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.5432 - val_pre_intent_loss: 0.3345 - val_pre_ner_loss: 0.2087 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 221/500\n",
      "70/70 [==============================] - 0s 6ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5430 - val_pre_intent_loss: 0.3345 - val_pre_ner_loss: 0.2085 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 222/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5441 - val_pre_intent_loss: 0.3359 - val_pre_ner_loss: 0.2082 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 223/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5448 - val_pre_intent_loss: 0.3367 - val_pre_ner_loss: 0.2081 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 224/500\n",
      "70/70 [==============================] - 0s 7ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5451 - val_pre_intent_loss: 0.3362 - val_pre_ner_loss: 0.2089 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 225/500\n",
      "70/70 [==============================] - 0s 7ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5464 - val_pre_intent_loss: 0.3374 - val_pre_ner_loss: 0.2090 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 226/500\n",
      "70/70 [==============================] - 0s 6ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5464 - val_pre_intent_loss: 0.3377 - val_pre_ner_loss: 0.2088 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 227/500\n",
      "70/70 [==============================] - 1s 8ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5465 - val_pre_intent_loss: 0.3380 - val_pre_ner_loss: 0.2085 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 228/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5461 - val_pre_intent_loss: 0.3368 - val_pre_ner_loss: 0.2093 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 229/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5484 - val_pre_intent_loss: 0.3390 - val_pre_ner_loss: 0.2094 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 230/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9920 - pre_ner_accuracy: 0.9966 - val_loss: 0.5492 - val_pre_intent_loss: 0.3399 - val_pre_ner_loss: 0.2093 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 231/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0112 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9961 - val_loss: 0.5484 - val_pre_intent_loss: 0.3386 - val_pre_ner_loss: 0.2098 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 232/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5484 - val_pre_intent_loss: 0.3396 - val_pre_ner_loss: 0.2088 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 233/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5491 - val_pre_intent_loss: 0.3405 - val_pre_ner_loss: 0.2086 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 234/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5494 - val_pre_intent_loss: 0.3397 - val_pre_ner_loss: 0.2097 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 235/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0113 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0042 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.5506 - val_pre_intent_loss: 0.3409 - val_pre_ner_loss: 0.2097 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 236/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5509 - val_pre_intent_loss: 0.3408 - val_pre_ner_loss: 0.2101 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 237/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.5516 - val_pre_intent_loss: 0.3417 - val_pre_ner_loss: 0.2099 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 238/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5520 - val_pre_intent_loss: 0.3423 - val_pre_ner_loss: 0.2097 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 239/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5520 - val_pre_intent_loss: 0.3422 - val_pre_ner_loss: 0.2098 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 240/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5543 - val_pre_intent_loss: 0.3438 - val_pre_ner_loss: 0.2105 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 241/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9969 - val_loss: 0.5552 - val_pre_intent_loss: 0.3448 - val_pre_ner_loss: 0.2104 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 242/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5563 - val_pre_intent_loss: 0.3459 - val_pre_ner_loss: 0.2105 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9730\n",
      "Epoch 243/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.5557 - val_pre_intent_loss: 0.3456 - val_pre_ner_loss: 0.2101 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 244/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5561 - val_pre_intent_loss: 0.3455 - val_pre_ner_loss: 0.2106 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 245/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.5565 - val_pre_intent_loss: 0.3460 - val_pre_ner_loss: 0.2104 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 246/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5575 - val_pre_intent_loss: 0.3464 - val_pre_ner_loss: 0.2111 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 247/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5580 - val_pre_intent_loss: 0.3463 - val_pre_ner_loss: 0.2117 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 248/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5593 - val_pre_intent_loss: 0.3483 - val_pre_ner_loss: 0.2110 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 249/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9971 - val_loss: 0.5600 - val_pre_intent_loss: 0.3488 - val_pre_ner_loss: 0.2113 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 250/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9955 - pre_ner_accuracy: 0.9971 - val_loss: 0.5587 - val_pre_intent_loss: 0.3476 - val_pre_ner_loss: 0.2111 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 251/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5632 - val_pre_intent_loss: 0.3500 - val_pre_ner_loss: 0.2132 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 252/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0111 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5616 - val_pre_intent_loss: 0.3497 - val_pre_ner_loss: 0.2119 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 253/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5610 - val_pre_intent_loss: 0.3481 - val_pre_ner_loss: 0.2129 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 254/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5621 - val_pre_intent_loss: 0.3490 - val_pre_ner_loss: 0.2131 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 255/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0110 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0040 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5630 - val_pre_intent_loss: 0.3502 - val_pre_ner_loss: 0.2127 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9728\n",
      "Epoch 256/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0112 - pre_intent_loss: 0.0071 - pre_ner_loss: 0.0041 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5649 - val_pre_intent_loss: 0.3521 - val_pre_ner_loss: 0.2129 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 257/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5640 - val_pre_intent_loss: 0.3514 - val_pre_ner_loss: 0.2127 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 258/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5638 - val_pre_intent_loss: 0.3512 - val_pre_ner_loss: 0.2126 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 259/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5654 - val_pre_intent_loss: 0.3522 - val_pre_ner_loss: 0.2132 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 260/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5652 - val_pre_intent_loss: 0.3521 - val_pre_ner_loss: 0.2131 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 261/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.5653 - val_pre_intent_loss: 0.3523 - val_pre_ner_loss: 0.2129 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 262/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5658 - val_pre_intent_loss: 0.3527 - val_pre_ner_loss: 0.2131 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 263/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9964 - val_loss: 0.5658 - val_pre_intent_loss: 0.3526 - val_pre_ner_loss: 0.2133 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 264/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5659 - val_pre_intent_loss: 0.3524 - val_pre_ner_loss: 0.2135 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 265/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.5670 - val_pre_intent_loss: 0.3531 - val_pre_ner_loss: 0.2139 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 266/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5672 - val_pre_intent_loss: 0.3536 - val_pre_ner_loss: 0.2136 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 267/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5690 - val_pre_intent_loss: 0.3549 - val_pre_ner_loss: 0.2140 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 268/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5678 - val_pre_intent_loss: 0.3541 - val_pre_ner_loss: 0.2137 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 269/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5679 - val_pre_intent_loss: 0.3544 - val_pre_ner_loss: 0.2135 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 270/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5688 - val_pre_intent_loss: 0.3549 - val_pre_ner_loss: 0.2139 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 271/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9965 - val_loss: 0.5697 - val_pre_intent_loss: 0.3559 - val_pre_ner_loss: 0.2138 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 272/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5699 - val_pre_intent_loss: 0.3557 - val_pre_ner_loss: 0.2142 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 273/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.5704 - val_pre_intent_loss: 0.3562 - val_pre_ner_loss: 0.2142 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 274/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5700 - val_pre_intent_loss: 0.3561 - val_pre_ner_loss: 0.2140 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 275/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9963 - val_loss: 0.5716 - val_pre_intent_loss: 0.3568 - val_pre_ner_loss: 0.2148 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9712\n",
      "Epoch 276/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9966 - val_loss: 0.5720 - val_pre_intent_loss: 0.3574 - val_pre_ner_loss: 0.2146 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 277/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.5709 - val_pre_intent_loss: 0.3563 - val_pre_ner_loss: 0.2146 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 278/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5715 - val_pre_intent_loss: 0.3570 - val_pre_ner_loss: 0.2145 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 279/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5721 - val_pre_intent_loss: 0.3574 - val_pre_ner_loss: 0.2147 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 280/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5716 - val_pre_intent_loss: 0.3570 - val_pre_ner_loss: 0.2146 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 281/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5722 - val_pre_intent_loss: 0.3577 - val_pre_ner_loss: 0.2145 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 282/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5723 - val_pre_intent_loss: 0.3574 - val_pre_ner_loss: 0.2149 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 283/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5740 - val_pre_intent_loss: 0.3585 - val_pre_ner_loss: 0.2155 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 284/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5731 - val_pre_intent_loss: 0.3582 - val_pre_ner_loss: 0.2149 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 285/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5737 - val_pre_intent_loss: 0.3587 - val_pre_ner_loss: 0.2150 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 286/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5735 - val_pre_intent_loss: 0.3586 - val_pre_ner_loss: 0.2149 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 287/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9966 - val_loss: 0.5736 - val_pre_intent_loss: 0.3586 - val_pre_ner_loss: 0.2149 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 288/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5748 - val_pre_intent_loss: 0.3596 - val_pre_ner_loss: 0.2152 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 289/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9966 - val_loss: 0.5743 - val_pre_intent_loss: 0.3590 - val_pre_ner_loss: 0.2153 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 290/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9972 - val_loss: 0.5758 - val_pre_intent_loss: 0.3599 - val_pre_ner_loss: 0.2159 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 291/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5755 - val_pre_intent_loss: 0.3598 - val_pre_ner_loss: 0.2157 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 292/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5764 - val_pre_intent_loss: 0.3605 - val_pre_ner_loss: 0.2159 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 293/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5759 - val_pre_intent_loss: 0.3602 - val_pre_ner_loss: 0.2157 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 294/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5766 - val_pre_intent_loss: 0.3606 - val_pre_ner_loss: 0.2159 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 295/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9964 - val_loss: 0.5764 - val_pre_intent_loss: 0.3604 - val_pre_ner_loss: 0.2160 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 296/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9967 - val_loss: 0.5771 - val_pre_intent_loss: 0.3611 - val_pre_ner_loss: 0.2159 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 297/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5769 - val_pre_intent_loss: 0.3609 - val_pre_ner_loss: 0.2160 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9714\n",
      "Epoch 298/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5776 - val_pre_intent_loss: 0.3612 - val_pre_ner_loss: 0.2164 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 299/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5782 - val_pre_intent_loss: 0.3616 - val_pre_ner_loss: 0.2167 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 300/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5779 - val_pre_intent_loss: 0.3614 - val_pre_ner_loss: 0.2165 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 301/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5771 - val_pre_intent_loss: 0.3613 - val_pre_ner_loss: 0.2158 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 302/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0107 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5778 - val_pre_intent_loss: 0.3615 - val_pre_ner_loss: 0.2164 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 303/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5780 - val_pre_intent_loss: 0.3619 - val_pre_ner_loss: 0.2161 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 304/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0039 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9968 - val_loss: 0.5774 - val_pre_intent_loss: 0.3612 - val_pre_ner_loss: 0.2163 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9725\n",
      "Epoch 305/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0070 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5792 - val_pre_intent_loss: 0.3624 - val_pre_ner_loss: 0.2168 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 306/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0108 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5787 - val_pre_intent_loss: 0.3622 - val_pre_ner_loss: 0.2165 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 307/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5788 - val_pre_intent_loss: 0.3622 - val_pre_ner_loss: 0.2167 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 308/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.5794 - val_pre_intent_loss: 0.3626 - val_pre_ner_loss: 0.2167 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 309/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5795 - val_pre_intent_loss: 0.3627 - val_pre_ner_loss: 0.2168 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 310/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5792 - val_pre_intent_loss: 0.3624 - val_pre_ner_loss: 0.2168 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 311/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.5789 - val_pre_intent_loss: 0.3622 - val_pre_ner_loss: 0.2167 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 312/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9968 - val_loss: 0.5792 - val_pre_intent_loss: 0.3623 - val_pre_ner_loss: 0.2169 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 313/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5793 - val_pre_intent_loss: 0.3623 - val_pre_ner_loss: 0.2170 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 314/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5795 - val_pre_intent_loss: 0.3626 - val_pre_ner_loss: 0.2169 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 315/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9967 - val_loss: 0.5797 - val_pre_intent_loss: 0.3628 - val_pre_ner_loss: 0.2169 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 316/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9965 - val_loss: 0.5798 - val_pre_intent_loss: 0.3627 - val_pre_ner_loss: 0.2171 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 317/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.5802 - val_pre_intent_loss: 0.3632 - val_pre_ner_loss: 0.2170 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 318/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5804 - val_pre_intent_loss: 0.3632 - val_pre_ner_loss: 0.2173 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 319/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9964 - val_loss: 0.5804 - val_pre_intent_loss: 0.3632 - val_pre_ner_loss: 0.2171 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 320/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5807 - val_pre_intent_loss: 0.3635 - val_pre_ner_loss: 0.2172 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 321/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9965 - val_loss: 0.5803 - val_pre_intent_loss: 0.3632 - val_pre_ner_loss: 0.2171 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 322/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9967 - val_loss: 0.5806 - val_pre_intent_loss: 0.3631 - val_pre_ner_loss: 0.2175 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 323/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9929 - pre_ner_accuracy: 0.9965 - val_loss: 0.5808 - val_pre_intent_loss: 0.3633 - val_pre_ner_loss: 0.2175 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 324/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5811 - val_pre_intent_loss: 0.3637 - val_pre_ner_loss: 0.2174 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 325/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5813 - val_pre_intent_loss: 0.3638 - val_pre_ner_loss: 0.2174 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 326/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5813 - val_pre_intent_loss: 0.3636 - val_pre_ner_loss: 0.2176 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 327/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5813 - val_pre_intent_loss: 0.3636 - val_pre_ner_loss: 0.2177 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 328/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5813 - val_pre_intent_loss: 0.3637 - val_pre_ner_loss: 0.2175 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 329/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5812 - val_pre_intent_loss: 0.3636 - val_pre_ner_loss: 0.2175 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 330/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5813 - val_pre_intent_loss: 0.3639 - val_pre_ner_loss: 0.2175 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 331/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5816 - val_pre_intent_loss: 0.3639 - val_pre_ner_loss: 0.2177 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 332/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9964 - val_loss: 0.5814 - val_pre_intent_loss: 0.3637 - val_pre_ner_loss: 0.2177 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 333/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5822 - val_pre_intent_loss: 0.3643 - val_pre_ner_loss: 0.2179 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 334/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9963 - val_loss: 0.5823 - val_pre_intent_loss: 0.3646 - val_pre_ner_loss: 0.2177 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 335/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5818 - val_pre_intent_loss: 0.3640 - val_pre_ner_loss: 0.2178 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 336/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5818 - val_pre_intent_loss: 0.3638 - val_pre_ner_loss: 0.2180 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 337/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.5821 - val_pre_intent_loss: 0.3641 - val_pre_ner_loss: 0.2179 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 338/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5821 - val_pre_intent_loss: 0.3641 - val_pre_ner_loss: 0.2181 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 339/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5823 - val_pre_intent_loss: 0.3644 - val_pre_ner_loss: 0.2180 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 340/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9964 - val_loss: 0.5828 - val_pre_intent_loss: 0.3646 - val_pre_ner_loss: 0.2181 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 341/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5819 - val_pre_intent_loss: 0.3639 - val_pre_ner_loss: 0.2180 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 342/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5824 - val_pre_intent_loss: 0.3643 - val_pre_ner_loss: 0.2181 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 343/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9966 - val_loss: 0.5827 - val_pre_intent_loss: 0.3644 - val_pre_ner_loss: 0.2183 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 344/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.5823 - val_pre_intent_loss: 0.3641 - val_pre_ner_loss: 0.2181 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 345/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5826 - val_pre_intent_loss: 0.3644 - val_pre_ner_loss: 0.2182 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 346/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9965 - val_loss: 0.5832 - val_pre_intent_loss: 0.3647 - val_pre_ner_loss: 0.2185 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 347/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9966 - val_loss: 0.5829 - val_pre_intent_loss: 0.3647 - val_pre_ner_loss: 0.2183 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 348/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0038 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5828 - val_pre_intent_loss: 0.3642 - val_pre_ner_loss: 0.2186 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 349/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5833 - val_pre_intent_loss: 0.3648 - val_pre_ner_loss: 0.2185 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 350/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.5829 - val_pre_intent_loss: 0.3643 - val_pre_ner_loss: 0.2185 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 351/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5832 - val_pre_intent_loss: 0.3649 - val_pre_ner_loss: 0.2183 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 352/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5835 - val_pre_intent_loss: 0.3647 - val_pre_ner_loss: 0.2187 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 353/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5830 - val_pre_intent_loss: 0.3644 - val_pre_ner_loss: 0.2186 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 354/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.5842 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2186 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 355/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9972 - val_loss: 0.5839 - val_pre_intent_loss: 0.3651 - val_pre_ner_loss: 0.2189 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 356/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5836 - val_pre_intent_loss: 0.3649 - val_pre_ner_loss: 0.2187 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 357/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5838 - val_pre_intent_loss: 0.3652 - val_pre_ner_loss: 0.2186 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 358/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5838 - val_pre_intent_loss: 0.3650 - val_pre_ner_loss: 0.2188 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 359/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5838 - val_pre_intent_loss: 0.3650 - val_pre_ner_loss: 0.2188 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 360/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5838 - val_pre_intent_loss: 0.3650 - val_pre_ner_loss: 0.2187 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 361/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5838 - val_pre_intent_loss: 0.3650 - val_pre_ner_loss: 0.2188 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 362/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9966 - val_loss: 0.5837 - val_pre_intent_loss: 0.3649 - val_pre_ner_loss: 0.2188 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 363/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.5838 - val_pre_intent_loss: 0.3650 - val_pre_ner_loss: 0.2188 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 364/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0106 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5835 - val_pre_intent_loss: 0.3647 - val_pre_ner_loss: 0.2188 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 365/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9964 - val_loss: 0.5838 - val_pre_intent_loss: 0.3650 - val_pre_ner_loss: 0.2188 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 366/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5840 - val_pre_intent_loss: 0.3651 - val_pre_ner_loss: 0.2189 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 367/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5842 - val_pre_intent_loss: 0.3652 - val_pre_ner_loss: 0.2190 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 368/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9970 - val_loss: 0.5843 - val_pre_intent_loss: 0.3652 - val_pre_ner_loss: 0.2190 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 369/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9969 - val_loss: 0.5838 - val_pre_intent_loss: 0.3649 - val_pre_ner_loss: 0.2189 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 370/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.5839 - val_pre_intent_loss: 0.3649 - val_pre_ner_loss: 0.2190 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 371/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5839 - val_pre_intent_loss: 0.3649 - val_pre_ner_loss: 0.2190 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 372/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9967 - val_loss: 0.5842 - val_pre_intent_loss: 0.3651 - val_pre_ner_loss: 0.2191 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 373/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5843 - val_pre_intent_loss: 0.3652 - val_pre_ner_loss: 0.2190 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 374/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5846 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2191 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 375/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5845 - val_pre_intent_loss: 0.3653 - val_pre_ner_loss: 0.2192 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 376/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5845 - val_pre_intent_loss: 0.3652 - val_pre_ner_loss: 0.2193 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 377/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5846 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2192 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 378/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5845 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2192 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 379/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5846 - val_pre_intent_loss: 0.3653 - val_pre_ner_loss: 0.2193 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 380/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5846 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2192 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 381/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9966 - val_loss: 0.5846 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2192 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 382/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9965 - val_loss: 0.5848 - val_pre_intent_loss: 0.3655 - val_pre_ner_loss: 0.2193 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 383/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5846 - val_pre_intent_loss: 0.3653 - val_pre_ner_loss: 0.2193 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 384/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9924 - pre_ner_accuracy: 0.9968 - val_loss: 0.5847 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2193 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 385/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5846 - val_pre_intent_loss: 0.3653 - val_pre_ner_loss: 0.2193 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 386/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5848 - val_pre_intent_loss: 0.3655 - val_pre_ner_loss: 0.2193 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 387/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.5849 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2195 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 388/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9969 - val_loss: 0.5849 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2195 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 389/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5848 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2194 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 390/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9968 - val_loss: 0.5846 - val_pre_intent_loss: 0.3652 - val_pre_ner_loss: 0.2194 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 391/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5849 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2196 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 392/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5849 - val_pre_intent_loss: 0.3653 - val_pre_ner_loss: 0.2195 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9723\n",
      "Epoch 393/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.5846 - val_pre_intent_loss: 0.3651 - val_pre_ner_loss: 0.2195 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 394/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5850 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2196 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9717\n",
      "Epoch 395/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5851 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2196 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 396/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9968 - val_loss: 0.5852 - val_pre_intent_loss: 0.3655 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 397/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.5853 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 398/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5852 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2195 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 399/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9967 - val_loss: 0.5853 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2196 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 400/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5855 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2198 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 401/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5851 - val_pre_intent_loss: 0.3654 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 402/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0069 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9968 - val_loss: 0.5853 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 403/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.5852 - val_pre_intent_loss: 0.3655 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 404/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5853 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 405/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9933 - pre_ner_accuracy: 0.9967 - val_loss: 0.5853 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 406/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9965 - val_loss: 0.5854 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 407/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5853 - val_pre_intent_loss: 0.3655 - val_pre_ner_loss: 0.2197 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 408/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5855 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2198 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 409/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9972 - val_loss: 0.5854 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2198 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 410/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9967 - val_loss: 0.5855 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2199 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 411/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5855 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2199 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 412/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5854 - val_pre_intent_loss: 0.3655 - val_pre_ner_loss: 0.2199 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 413/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5854 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2198 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 414/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5855 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2199 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 415/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5855 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2199 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 416/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5854 - val_pre_intent_loss: 0.3655 - val_pre_ner_loss: 0.2199 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 417/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5856 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 418/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9970 - val_loss: 0.5855 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2199 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 419/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9969 - val_loss: 0.5856 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 420/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5856 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2199 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 421/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9968 - val_loss: 0.5855 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 422/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5856 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 423/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5857 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 424/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5857 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 425/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5856 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 426/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9966 - val_loss: 0.5857 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2201 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 427/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9968 - val_loss: 0.5856 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 428/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9967 - val_loss: 0.5858 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2201 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 429/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5857 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2200 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9721\n",
      "Epoch 430/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.5857 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2201 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 431/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5858 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2201 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 432/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5859 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2202 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 433/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5858 - val_pre_intent_loss: 0.3656 - val_pre_ner_loss: 0.2201 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 434/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5858 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2201 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 435/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5859 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2201 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 436/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9968 - val_loss: 0.5858 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2201 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 437/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9966 - val_loss: 0.5860 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2202 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 438/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9967 - val_loss: 0.5859 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2202 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 439/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9968 - val_loss: 0.5860 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2202 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 440/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.5860 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2202 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 441/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5860 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2202 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 442/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5859 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2202 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 443/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9968 - val_loss: 0.5859 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 444/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9966 - val_loss: 0.5861 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 445/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5860 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 446/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5860 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 447/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5861 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 448/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5860 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 449/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5861 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 450/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5860 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 451/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 452/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 453/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5861 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9375 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 454/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5863 - val_pre_intent_loss: 0.3659 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 455/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2203 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 456/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0037 - pre_intent_accuracy: 0.9937 - pre_ner_accuracy: 0.9971 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 457/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 458/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 459/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 460/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 461/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 462/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5862 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 463/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5862 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 464/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 465/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5862 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 466/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5862 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 467/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9969 - val_loss: 0.5861 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 468/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5862 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 469/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9972 - val_loss: 0.5862 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2204 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 470/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5862 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 471/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9970 - val_loss: 0.5863 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 472/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5862 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 473/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9971 - val_loss: 0.5862 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 474/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5862 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 475/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.5862 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 476/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.5863 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 477/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5862 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 478/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9972 - val_loss: 0.5862 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 479/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9955 - pre_ner_accuracy: 0.9969 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 480/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9968 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 481/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5863 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 482/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9969 - val_loss: 0.5863 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 483/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5863 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 484/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5863 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 485/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5863 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 486/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 487/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 488/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9973 - val_loss: 0.5863 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 489/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9969 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 490/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5863 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2205 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 491/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5863 - val_pre_intent_loss: 0.3657 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 492/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9969 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 493/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9942 - pre_ner_accuracy: 0.9969 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 494/500\n",
      "70/70 [==============================] - 0s 4ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9972 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 495/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 496/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9971 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 497/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9970 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 498/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9971 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 499/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9946 - pre_ner_accuracy: 0.9970 - val_loss: 0.5864 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n",
      "Epoch 500/500\n",
      "70/70 [==============================] - 0s 5ms/step - loss: 0.0105 - pre_intent_loss: 0.0068 - pre_ner_loss: 0.0036 - pre_intent_accuracy: 0.9951 - pre_ner_accuracy: 0.9969 - val_loss: 0.5865 - val_pre_intent_loss: 0.3658 - val_pre_ner_loss: 0.2206 - val_pre_intent_accuracy: 0.9330 - val_pre_ner_accuracy: 0.9719\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f8a3c0bf190>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_dataset,epochs=params['epochs'],validation_data=valid_dataset,callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# model.fit(tarin_seq, [train_intent, train_ner],epochs=params['epochs'],validation_split=0.1,callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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